Does a child's mid‐upper arm circumference‐for‐age z‐score represent another nutritional indicator of childhood malnutrition status?

Abstract Child wasting is defined as a weight‐for‐height/length z‐score (WLZ/WHZ) < −2, and this indicator of nutritional status is used worldwide. However, a precise measurement is required for the assessment of a child's nutritional status, which may not always be possible due to expensive instruments, especially in poor resource settings. In some instances, mid‐upper arm circumference‐for‐age z‐score (MUACZ) is also being used for screening purposes, which is a simple and useful nutritional indicator. The objective of this paper is to identify the optimal cut‐off point for the MUACZ to identify wasted children, and also to determine if the same factors are associated with MUACZ and wasting. Data were derived from the Suchana evaluation data. The optimal cut‐off value was estimated via receiver operating characteristic (ROC) curve analysis using acute malnutrition as a gold standard with maximum sensitivity and specificity. Multiple logistic regression was used to assess the associated factors with the MUACZ. Using the gold standard indicator of nonwasting (WLZ ≥ −2), a positive outcome, the optimal cut‐off point for the MUACZ was −1.27. The area under the ROC curve was 0.88, indicating that the model had a power of 88% to differentiate between the positive and negative classes. It implies that a child's MUACZ was correlated with WLZ, and a MUACZ below −1.27 appeared to accurately identify wasting among children aged 3–23 months. MUACZ < −1.27 might be another useful indicator of childhood wasting than a WLZ < −2.


| INTRODUCTION
Childhood wasting, a major public health concern, is defined as a weight-for-height/length z-score (WLZ/WHZ) < −2. The WLZ/WHZ is widely used globally as an indicator of nutritional status in children (Bari et al., 2019). If not identified in a timely manner, childhood acute malnutrition can be further deteriorated in severity. Growth monitoring and promotion programmes are widely conducted in low-and middle-income countries (LMICs) to assess nutritional status by measuring children's weight and height at health care centres (Liu et al., 2017). However, these measurement tools have several drawbacks and practical limitations for rapid assessment, especially for debilitated, acutely ill or disabled children (Haque et al., 2021). Furthermore, anthropometric techniques are prone to inaccuracies, which could occur from inadequate personnel training, for instance. In a previous cohort study, an anthropometric measurement requirement, such as observer training and data collection supervision, was shown to be more difficult to implement than first anticipated. In a situation where qualified supervisors and constant training are not available, the greater necessity for sustained training would jeopardize its effectiveness (Sicotte et al., 2010). Moreover, it can be difficult to accurately measure the length and weight of children under 24 months old.
Another limitation is that the instruments used to measure length and weight might not always be precise. Moreover, high-precision instruments are very expensive in resource-constrained settings or population-based surveys (Haque et al., 2021). Mid-upper arm circumference (MUAC), a common anthropometric measurement, is being used especially in emergency and crisis settings to assess the nutritional status of children, which might be an alternative option to measure WLZ/WHZ (Briend et al., 1987;Sultana et al., 2015; World Health Organization (WHO) Working Group, 1986). Children with a MUAC of less than 11 cm are categorized as malnourished (Bari et al., 2019;Briend et al., 1987;Fernández et al., 2010;Hossain et al., 2017;Takyi et al., 2020). Literature suggested several cut-off values for MUAC, which range from <11 to 12.5 cm (WHO, 2009).
However, the MUAC is highly correlated with age and sex.
Thus, an age-and sex-specific indicator might be more reliable.
Several kinds of literature also suggested that the MUAC-for-age z-score (MUACZ) could be an independent indicator of child malnutrition, which is less expensive and also gives similar estimates as WLZ (Becker et al., 2014;Sadler et al., 2011;Stephens et al., 2018). Therefore, MUACZ may represent an alternative indicator for defining a child's nutritional status. MUACZ is calculated as a z-score scale using the 2006 WHO Standards for Children, as (observed MUAC value − average value of the reference population)/standard deviation value of reference population (Custodio et al., 2018;Haque et al., 2019;Miller et al., 2019;Onis & Bloessner, 1997). However, as far as we are aware, no large-scale study has been conducted to identify the optimal MUACZ cut-off point with the highest sensitivity and specificity in the context of Bangladesh.
Furthermore, for the establishment of using MUACZ, it is also needed to compare if MUACZ-associated factors are the same as factors that are associated with WLZ. In this regard, there are several factors, for example, sex, age, morbidity, infant and young child feeding practices, maternal age, health care practices, unimproved place of delivery, dietary diversity, household food insecurity, socioeconomic status, unimproved water and sanitation, unimproved household materials and the level of education of the household head (Choudhury et al., 2017;Khan & Kraemer, 2009;MAL-ED Network Investigators, 2017;Platts-Mills et al., 2017), which are associated with child nutritional status (Choudhury et al., 2017;Khan & Kraemer, 2009;MAL-ED Network Investigators, 2017;Platts-Mills et al., 2017). These factors were also found similar while MUACZ was used as a continuous variable. In the programmatic aspect, it is always desirable to get one cut-off point, which might be more user-friendly.
Thus, the objective of this study is to identify the optimal cut-off point for MUACZ to classify with wasting and to examine the associated factors if they are similar to WLZ.

| Study design and population
The large-scale Suchana development programme was conducted among the most susceptible households in vulnerable villages in the Sylhet division, located in the northeast region of Bangladesh.
Data were obtained for this paper from the Suchana evaluation data Haque et al., 2020). Suchana evaluation was a pre-post randomized cluster design with two cross-sectional surveys. Baseline data were collected from mother-child dyads in the year 2016, whereas the endline data were collected in the year 2019. The age of the children during enrolment was 0-23 months. For endline data collection, new mother-child dyads were recruited where the age of the children was the same as that during enrolment.

Key messages
• We calculated the cut-off point for a child's mid-upper arm circumference-for-age z-score (MUACZ) that MUACZ below −1.27 appears to accurately identify acute malnutrition among children aged 3-23 months with the highest sensitivity and specificity.
• Measurement of MUAC would be especially helpful when large numbers of children need to be screened quickly, particularly in humanitarian aid settings.
• MUACZ < −1.27 may represent a more useful indicator of childhood malnutrition than a weight-for-length z-score < −2.

| Sample size
The sample size was calculated for this study based on the main objective of Suchana evaluation. The objectives of Suchana programme were to reduce childhood stunting and improve exclusive breastfeeding and a minimum acceptable diet. The calculated sample size was 16,162 (baseline: 5440; endline: 10,722) . We excluded 38 cases from the database due to anthropometry measurement errors. We also excluded 1281 cases for the children aged less than 3 months, owing to MUACZ being not applicable for children aged 0-3 months. Finally, we analysed 14,843 cases for this paper.

| Data collection
Android tablets complemented by custom-developed Java software were employed for data collection during the surveys. The mobilebased data collection process reduced the data entry burden, as the data were entered at the interviewer level and the records were uploaded to a server at the icddr,b using the built-in internet connectivity of the devices. This allowed the data analysis team to review the consistency of the data every day. The Java softwarebased electronic questionnaire was designed as survey forms in both Bangla and English languages, which were interchangeable at any time during the data collection process. The enumerators used the Bangla form on the PDA while interviewing the respondents and recording anthropometric measurements. A standard operating procedure was provided to all staff. Editing and updating, range, consistency, frequency and duplication checks and cross-tabulation of the data were regularly performed using the data entry period.
Unusual observations were discussed and resolved on a daily basis. A secure web-based data management system was used to manage the data. When data collection was complete, the data were transferred into Stata software release 14 (StataCorp) to define the variable and value labels .
SECA 874 weight scales with an accuracy of 1 g were used to measure maternal and child weight. The mother was asked to remove all jewellery and accessories and wear minimum culturally acceptable clothing before standing on the weight scale. Then, the mother was asked to hold her child and stand on the scale. Two consecutive readings were taken; if the difference between readings was more than 50 g, a third measurement was taken and then averaged. Then, the mother was requested to stand alone on the weight scale and the weighing process was repeated. The difference in weight between the mother and child and mother was calculated automatically by the survey software and logged as the weight of the child. SECA 416 infantometers with a precision of 0.1 cm were used to measure the children's length; a third measurement was taken if the first two consecutive measurements differed by more than 2 cm. MUAC is measured at the mid-point between the tip of the shoulder and the tip of the elbow (olecranon process and acromion process). Child's age and sex-specific anthropometry indicators, such as length-for-age, weight-for-age, weight-for-length and MUACZ, were calculated using the 2006 WHO Standards for Children, as (observed anthropometry value − average value of the reference population)/standard deviation value of reference population (Custodio et al., 2018;Haque et al., 2019;Miller et al., 2019;Onis & Bloessner, 1997). Child's weight in kilogram, length in centimetre, MUAC in centimetre, birth date and survey data were entered in the WHO Anthro (version 3.2.2) software to calculate the z-scores. Children were defined as stunted if their lengthfor-age is less than minus two standard deviations (LAZ < −2), wasted if their weight-for-length is less than minus two standard deviations (WLZ < −2) and underweight if their weight-for-age is less than minus two standard deviations (WAZ < −2) (Haque et al., 2021).

| Outcome variable
The primary outcome variable was children's MUACZ. The optimal cut-off value was estimated through receiver operating characteristic (ROC) curve analysis as well as Youden's index using acute malnutrition as the gold standard with maximum sensitivity and specificity. A child was labelled as wasted if his/her MUACZ is less than the optimal cut-off point.

| Independent variables
A list of independent variables was finalized through a literature review and descriptive analysis. The conceptual framework for this study is shown in Figure 1. The children's characteristics were age, sex, experience of any illness in the last 15 days and age-appropriate breastfeeding. Age-appropriate breastfeeding was defined as infants 0-5 months of age who received only breast milk during the previous day, and children 6-23 months of age who received breast milk, as well as solid, semisolid or soft foods, during the previous day.
Maternal characteristics were age, MUAC < 24 cm as nutritional status, at least four antenatal care (ANC) visits by a skilled service provider, resting more than usual during last pregnancy , delivery in a health care facility, experience of domestic violence (Haque et al., 2020) and minimum dietary diversity for women (MDD-W). In our questionnaire, we asked women whether they rested more than usual while pregnant with their most recent child. We had three options: (i) more, (ii) as before and (iii) less.
'More' was considered as the indicator of 'resting more than usual during the last pregnancy'. Experience of domestic violence was defined as the husband threatening to divorce his wife or marry another woman, or if the husband or another family member had verbally or physically abused the participant at any point of time since their marriage. MDD-W was defined as mothers consuming at least five food groups, that is, the number of unique food items consumed in the last 24h by the target mother. The 10-food group scoring system was based on the FANTA III guideline (Daniels et al., 2009;FAO and FHI 360, 2016). The household characteristics were the water and soap/ash available within 30 feet of the toilet structure, HAQUE ET AL. | 3 of 12 Household Food Insecurity Access Scale (HFIAS), household type (poor/very poor), household size, type of latrine, type of floor and household head education. The HFIAS was determined using the 'Food and Nutrition Technical Assistance's Guideline' and categorized as food secure, mildly food insecure, moderately food insecure or severely food insecure (Coates et al., 2007). The household type (poor and very poor) was determined by a wealth ranking exercise, which was carried out to identify potential target beneficiary households .

| Statistical analysis
Data were visualized using histograms, bar diagrams, pie charts and scatter plots, as appropriate. Descriptive statistics were used to summarize data (frequency and proportion for categorical variables, mean and standard deviation for quantitative variables). The descriptive analyses focused on the indicator MUACZ.
Pearson's correlation coefficients (r) were calculated to assess the linear relationships between children's WLZ and MUACZ. ROC curve analysis for MUACZ was performed based on nonwasting (WLZ ≥ −2); a healthy child was defined as a positive outcome (Sultana et al., 2015).
The area under ROC curves was estimated to compare the aggregated classification performance of new indicators; the optimal MUACZ cutoff point was identified based on maximum sensitivity and specificity.
In general, an AUC of 0.5 suggests no discrimination, 0.7 -0.8 is considered acceptable, 0.8-0.9 is considered excellent and more than 0.9 is considered outstanding (Mandrekar, 2010). Our hypothesis is that the values of sensitivity and specificity would be close to 80%. We also applied Youden's index to calculate the empirical optimal cut-off point (Sultana et al., 2015).
Finally, we explored the associations between MUACZ and other indicators associated with childhood malnutrition in previous studies.
Simple logistic regression was used to assess the bivariate relationships between MUACZ as a categorical variable (MUACZ ≥ cut-off point denotes healthy and MUACZ < cut-off point denotes malnourished) and the other independent variables. Multiple logistic regression was employed to identify the factors independently associated with the outcome indicator after adjusting for union (i.e., the smallest unit of the Suchana study areas) as a cluster.
The variables were included in multiple regression analysis by stepwise forward selection if their p < 0.25 (Bursac et al., 2008); some indicators, such as age, sex and other relevant variables were included regardless of their p value. In the final model, p < 0.05 was considered the significance level. We also performed the same analysis for MUAC < 12.5 cm, MUAC < −2 and WLZ < −2 to compare with a new indicator.

| RESULTS
A total of 14,843 children (3-23 months) data was used for this analysis. Children below 3 months were not included in this analysis because the WHO Anthro Application calculated the MUACZ for F I G U R E 1 Conceptual framework of factors that affect childhood malnutrition. ANC, antenatal care; BF, breastfed; HFIAS, household food security access scale; HH, household; MDD-W, minimum dietary diversity for women; MUACZ, mid-upper arm circumference-for-age z-score; WLZ, weight-for-length z-score.
only the age group between 3 months and 5 years. The general characteristics of the children, women and households are presented in Table 1. Around 25.0% of children had a MUACZ < −1.27, whereas the prevalence of wasting was 7.5%, stunting was 44.0% and underweight was 28%. The correlation coefficient (Figure 2) between WLZ and MUACZ was very high (r = 0.69). In the ROC curve analyses using nonwasting (WLZ ≥ −2) as a gold standard indicator and a healthy child as a positive outcome, the optimal cut-off point for  We also determined the factors associated (  −2), MUAC < 12.5 cm and MUACZ < −2 using same multiple logistic regression analysis. From this model, we found that direction of strength of association for WLZ < −2 and MUACZ < −1.27 were similar but the status for MUAC < 12.5 cm and MUACZ < −2 was opposite for some important predictors, such as age and sex (Table 2).

| DISCUSSION
We assessed data generated from a large-scale survey data to determine whether the MUACZ represents an effective nutritional indicator and to identify the optimal cut-off point to distinguish between nonwasting and wasting children. Our results demonstrate a strong correlation between WLZ and MUACZ, similar to an earlier study (Stephens et al., 2018). When wasting was defined using the WLZ as the gold standard indicator, the optimal cut-off value for the MUACZ was −1.27; this cut-off had a high sensitivity (i.e., classifies nonwasted children as healthy) and specificity (i.e., classifies wasted children as malnourished). The area under the ROC curve was almost 88%, indicating that this MUACZ cut-off value exhibits excellent performance as an indicator of childhood malnutrition (Mandrekar, 2010). On the other hand, the sensitivities of the indicators MUAC < 12.5 cm and MUACZ < −2 were high but the values of specificities were less than 50%, which was not expected.
Some of the factors identified to be associated with MUACZ through multiple logistic regression are the most critical factors associated with child malnutrition (Choudhury et al., 2017;Rahman & Chowdhury, 2007;Vilcins et al., 2018). Our findings imply that age and sex are two key unmodifiable related factors, with older and male children being more wasted, which is consistent with earlier studies   (6066) reported that MUACZ effectively recognized severe acute malnutrition and moderate acute malnutrition (Miller et al., 2019).
However, MUAC can be easily measured in these cases, as only a measuring tape is needed to measure MUAC and then only a z-score calculation is required to identify the nutritional status of the children (Rasmussen et al., 2012). Indeed, measurement of MUAC would be especially helpful when large numbers of children need to be screened quickly, particularly in humanitarian aid settings.
However, if MUAC < 12.5 cm is used as a cut-off value to regress the outcome, then our regression analysis showed different results for age-and sex-specific outcomes, which is not similar to previous study findings where the outcome variable is wasting (Choudhury et al., 2017 F I G U R E 2 Scatterplot illustrating the relationship between children's weight-for-length z-scores and mid-upper arm circumference z-scores. F I G U R E 3 Screening test of WLZ (<−2) by MUACZ based on area under ROC curve analysis; a nonwasting child (WLZ ≥ −2.0 and MUACZ ≥ −1.27) was defined as a positive outcome in the ROC curve analyses. MUACZ, mid-upper arm circumference-for-age z-score; ROC, receiver operating characteristic; WLZ, weight-for-length z-score.

| Strength and limitations
We only covered extremely poor households in this study but we are confident of our study findings because of using an appropriate sampling method and a large sample size. Moreover, highly precise instruments were used to measure weight and length data.